17941121. HARDWARE-AWARE FEDERATED LEARNING simplified abstract (QUALCOMM Incorporated)
Contents
HARDWARE-AWARE FEDERATED LEARNING
Organization Name
Inventor(s)
Vijaya Datta Mayyuri of San Diego CA (US)
HARDWARE-AWARE FEDERATED LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 17941121 titled 'HARDWARE-AWARE FEDERATED LEARNING
Simplified Explanation
The abstract describes a method for hardware-aware federated learning, where a device receives information on a jointly-trained artificial neural network (ANN) from a server, determines its current hardware capability for on-device training, and transmits this information back to the server. The server then sends an adapted version of the ANN based on the device's hardware capability.
- Device receives information on a jointly-trained ANN from server
- Determines current hardware capability for on-device training
- Transmits hardware capability information to server
- Receives adapted version of ANN based on hardware capability from server
Potential Applications
- Personalized on-device training of artificial neural networks
- Adaptive learning models based on device hardware capabilities
Problems Solved
- Optimizing training of neural networks for different devices
- Improving efficiency and performance of on-device training
Benefits
- Customized training models for individual devices
- Enhanced performance and efficiency in on-device training
- Adaptation to varying hardware capabilities for improved learning outcomes
Original Abstract Submitted
A processor-implemented method for hardware-aware federated learning includes receiving, from a server, information corresponding to a first jointly-trained artificial neural network (ANN). A current hardware capability of a device for on-device training of the first jointly-trained ANN is determined. The device transmits an indication of the current hardware capability to the server. In response to the transmitted indication, the device receives information corresponding to a second jointly-trained ANN) from the server. The second jointly-trained ANN is an adapted version of the first jointly-trained ANN generated based on the indication of the current hardware capability.
